Language change is a complex social phenomenon, revealing
pathways of communication and sociocultural influence. But while language
change has long been a topic of study in sociolinguistics, traditional
linguistic research methods rely on circumstantial evidence, estimating the
direction of change from differences between older and younger speakers. In
this research, we use a data set of several million Twitter users to track
language changes in progress. First, we show that language change can be
viewed as a form of social influence: we observe complex contagion for
``netspeak'' abbreviations (e.g., lol) and phonetic spellings, but not for
older dialect markers from spoken language. Next, we test whether specific
types of social network connections are more influential than others, using
a parametric Hawkes process model. We find that tie strength plays an
important role: densely embedded social ties are significantly better
conduits of linguistic influence. Geographic locality appears to play a
more limited role: we find relatively little evidence to support the
hypothesis that individuals are more influenced by geographically local
social ties, even in the usage of geographical dialect markers.